Final thesis booklet 2017

D /A [A r c h i tecture Master Thesis]

ZNOD ES Airports Cr oss in g Po in t C u l tu re [Disc re te A utom a ti on]

Eid / Vu

Š 2017 Dessau International Architecture School

FH-Anhalt University of Applied Sciences

Master Thesis | Studio Nathan's House Studio Master: Prof. Krassimir Krastev Second Advisor: Prof. Eric Helter Z-Nodes Airports-Crossing Point Culture [Discrete Automation] Mohammed Eid I 4063198 Nguyen Tran Huy Vu I 4063071

In the spirit of A.I., Algorithmic design and Parametricism: "you can't delegate thinking, computer fail, checklists fail, everything can fail. But people can't". ...de Crespigny

Ac know ledgm ent

I would like to express all my gratitude to my family for their encouragement and support. I would also to thank my professors, friends and my colleagues for their constant support and inspiration.

0.00 Prologue

0 . 0 3 D e s i g n P r o p o s al

0.00.1 Ab s t r a c t . . . . . . . . . . . . . . . . . . . . . Vi i i

0.01 Prelude

C on ten ts

0 . 0 1. 1 hypothesis ................... 01 0 . 0 1. 2 K e y wo r d s . . . . . . . . . . . . . . . . . . . . . . 0 2 0 . 0 1. 3 Me t h o d o l o g y . . . . . . . . . . . . . . . . . 0 3

0 . 02 O p e r a t i o n s 0.02.1 Au t o ma t i o n . . . . . . . . . . . . . . . . . . .

0.02.2 Behavioural ..................

0.02.3 G e n e t i c Al g o r i t h m . . . . . . . .

0.02.4 Ma c h i n e L e a r n i n g . . . . . . . . .

0.02.5 D a t a D r i v e n F o r ms . . . . . . .

07 11 15 19 23

0 . 0 3 . 1 A irports 0 . 0 3 . 1. 1 cu l t u r e N o des . . . . . . . . . . . . . . . . 31 0 . 0 3 . 1. 2 Ma s t e r p l a n A nal ysi s . . . . . 39 0 . 0 3 . 1. 3 ca s e S t u d i es . . . . . . . . . . . . . . . . . 49

0.03.2 LCA 0.03.2.1 S t u d y o n Si t e . . . . . . . . . . . . . . . . 73 0.03.2.2 S t u d y o n Spac e . . . . . . . . . . . . . . 91 0.03.2.3 S t u d y o n F or m . . . . . . . . . . . . . . . 111

0 . 0 4 c o n clu s i o n 0.04.1 Vi s i o n . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 0.04.2 Imp l e me n t a t i o n s . . . . . . . . . . . . . . . . . . . . . . 126

0.05 credits B i b l i o g r a p h y . . . . . . .. . . . . . . . . . . . . . . . . . . . . 127

0. 00 P rologue Genetic Algorithm is a search method used in artificial intelligence and computing. It is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology.

Crowd Simulation is the process of simulating the movement (or dynamics) of a large number of entities or characters.

Genealogy

0.00.1

A b s tra ct

a plant's or animal's line of evolutionary development from earlier forms.

Vestigial structure is an anatomical feature that no longer seems to have a purpose in the current form of an organism of the given species. Often, these vestigial structures were organs that performed some important function in the organism at one point in the past. However, as the population changed due to natural selection, those structures became less and less necessary until they were rendered pretty much useless.

viii

Approach: This thesis posits an architecture in which two antithetical morphological systems - genetic algorithms and crowd simulation - are forced to dialogue in order to create an intelligent network for mixed use development as an application for the cities' airports as a crossing culture nodes. It argues for the necessity of the incorporation of both the top-down and bottom-up method in design.

Genealogy: Viewsheds and crowds, discrete Automation, Mutation and Vestigial Evolution.

Z-Nodes: Proposing airports as a crossing culture nodes that accommodate a different activities between different users with different cultures in the physical world, engaging this idea and taking it further to assume that airports shouldn't be isolated and should be close to cities' centers, this vision stands in counteractive with all social media networks as a virtual realm that provides the perfect connectivity between people and culture in virtual world.

The design process aims to Create a general strategy with multiple and flexible rules for designing airports to reconfigure the whole image of the isolated wide platform of the point to point departure to a crossing culture node.

The research also considers how the involvement of providing different interactions between users [passengers] in the design and how further would the design support an engaging and intelligent circulation and its interaction toward surrounded environment, and how it can adapt to the user activities.

The accuracy of prediction in the simulations determines how flexibility and connectivity we achieve in our multi-function networks.

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0. 00 P rologue

Flights paths MAP around the globe (Physical Nodes of interaction)

x

[Image Courtesy of Michael Markieta]

Facebook collaborations MAP around the globe [Image Courtesy of Olivier Beauchesne] (Virtual Nodes of interaction)

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0.01

p r elu de

0 . 0 1. 1 hypothesis 0 . 0 1. 2 K e y wo r d s 0 . 0 1. 3 Me t h o d o l o g y

0. 01 p relude

0.01 .1

h yp o the s is

01

Following the nature of information in digital architecture I argue for the necessity of the incorporation of both the top-down and bottom-up method in airport's terminal design, assuming that mapping activities inside terminal buildings should provide different interaction between passengers that create a flow of spatial experience supported with AI system and self-service technologies as a common platform between a multi-culture users that drive a new mantra of their interactions for the physical realm and away from the methodology of point to point departure in the program and design of airports. Airports architecture should be out of that context of heterotopic spaces or the functional transitory buildings but should support the sociocultural phenomenon as a very specific public space.

02

0. 01 p relude 0.01 .2

K ey w o rds Discrete Automation | Form Finding | Optimization | Genetic Algorithm | AI | Machine Learning | Crowd Algorithm | Airport |

0.01 .3

M etho d o l og y I Operation: Optimization III Operation: Behavioral Condition: Views Condition: Crowds / Movement Function: Creating a parametric Function: Using three-dimensional model for finding a generic form targeted agents simulation to predict movements in different based on basic site analysis, scenarios, developed to get the the proposed program, and targeted viewsheds. fewer intersections with fast and directed paths.

II Operation: Genetic Algorithm IV Operation: AI Condition: Activities pattern Condition: Form selection Function: Selection process, Function: Dividing the complex network in between different crossing over and mutation; Viewsheds quality, preliminary functions to small simple tasks as a way for developing an AI area plots, and the shortest distance between connections, aided design movement system between these activities and as a fitness values for the evaluation. functions.

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The process following a discrete automation within a vestigial structure, in which the simulation run first then cut at a random point, then adding more variations or taking the result at the cutting point then continuing as a pre-sketch for the next phase of design.

Choosing one of the airports where located close to a dense urban fabric and the city center. Evaluating it and making connectivity analysis, measuring how the airport connected with context with a study on the morphology of the built environment.

Mapping airports requirements and the available open spaces and the possibilities of interventions for the new proposal, integrating a network between the terminal building, train station, parking and the hotel, then focus on the terminal building.

The circulation inside terminals following a discrete process of flow in motion in which the user follow a discrete sequence of events in time in a linear strategy to pass in between the determined check points and gates.

Extracting and interpret variables from these spaces and circulation inside the terminal add on them viewsheds optimization analysis to feed it as an input for the form-finding variables.

After mapping the activities inside the terminal, mapping users movements will be determined, a new proposed plans will be configured to support more flexible space and circulation aided with Artificial intelligence system.

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0.02

O p er at io n s

0.02.1 Au t o ma t i on 0.02.2 B e h a v i o u r al 0.02.3 G e n e t i c A l gor i t hm 0.02.4 Ma c h i n e L ear ni ng 0.02.5 D a t a D r i v en For m s

0. 02 O perations

0.02.1

A u tom a tion

07

A self-operating machine, or a machine or control mechanism designed to automatically follow a predetermined sequence of operations, or respond to predetermined instructions. a full systems designed to be automated, managed to be handeled by an autonomous agents, another kind of brains that can evolve thinking and make decisions under the sence of Artificial intelligence. AI, as defined in Intelligent Agents Theory and Practice in 1994 as no doubt already, is an umbrella term for intelligent software and computing.

For software to be deemed intelligent, it needs to be capable of things like reasoning, knowledge, planning, learning, communication, and perception; in essence, it needs to be more human. Focusing into the relation between this encoding to a computer aided architectural design is determined by the specification of design criteria and the procedures for automatically generating design solutions. This approach in design requires an alternative representations of any architectural space into a mathematical and operational order, which is becomes more difficult and complex during the process of any architectural design. The difficult task that when a solution to an architectural design problem is produced by a computer, it initially exists in the form of a collection of symbols stored in the machine’s memory, it needs to be encoded into the physical reality of an implemented design.

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0. 02 O perations

0.02.1 Aut om at ion

William j. Mitchell

Alternative representations of a floor plan: (a) Simple outline floor plan (b) Integer array representation (c) Point vector representation (d) Dual graph. The integer array technique explicitly represents surfaces,

volumes, and empty space, whereas the point-vector technique implicitly represents surfaces, volumes, and empty space by defining the boundaries of spatial domains.

The Bartlett AD - RESEARCH CLUSTER 4, 2016 Gilles Retsin + Manuel Jimenez Garcia w. Vicente Soler

These digital blocks can be assembled with a robot in an easy pick and place manner where a gripper can grip the block from specifically designed gripping spots. These gripping spots define the geometry of the brick. The robotic blocks can also be tracked by cameras during the assembly process, so the robot has a understanding of the structure it is building, and can react to possible mistakes.

TECHNIQUES OF AUTOMATED DESIGN IN ARCHITECTURE

Tree -structured organization of information, then simple built form, and a list structure for representing the type of built form.

Tree diagram illustrating generation of alternative arrangements of edge-connected square modules

ICD/ITKE Research Pavilion 2016-17 Digitally fabricated pavilion

The pavilion was constructed with two different types of robots: flying drones and stationary machines. The drones were used to pass the fiber between the two stationary machines. The two types of robots communicated without the need for human intervention using an integrated sensor interface that collected real-time data

TECHNIQUES OF AUTOMATED DESIGN IN ARCHITECTURE A SURVEY AND EVALUATION

A SURVEY AND EVALUATION

William j. Mitchell

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10

0. 02 O perations

0.02.2

B e h a v io u ral

11

Behaviour means the range of actions and mannerisms made by individuals, systems, or artificial entities in conjunction with themselves or their environment, which include the other systems or organisms around as well as the physical environment.1 The word has related synonyms toward any design issue: functioning, action, performance, operation, running, reaction, response, and more.

Through behaviours a generic strategies can be identified, it affords organisational parameters to evolve through systemic forms of interaction, following a non-linear design strategy a behavioral patterns can be mapped and within it establishing rules of interaction of autonomous agents to create a preliminary design approach through a computer code that can grow, evolve and mutate architectural matter without the burden of preconceived ideas of top-down process. Proposing a decentralized connective network model developed through transcoding of agent-based systems and a simulation of multidisciplinary activities based on the motion as predefined inputs to create a pattern as a basic function of the agents‘ behavior that could incorporate into the design of circulation systems that are able to evolve and forecast future possibilities of different interactions.

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0. 02 O perations

input data : counting people on rush hour

0.02.2 B ehaviour al

convert people into vectors

Flocking simulation Artifically psycology grouping

Passenger behaviour

Tracing moving vectors

Translate into geometries

Line | line intersection

Line | dot intersection

Wet wool threads Frei Otto, soup film behaviour models

The goal is to develop a method which is suitable to determine a connecting system for any plane point constellation having the minimum sum length. The reorganization of the wet threads, due to the capillary water tension, follows the shortest and less energy consumption (stress consumption) configuration that the physical conditions permit.

Stigmergic Systems Armin Akbari, Iaac, Barcelona

Ants behaviour, in this project, agents are given some random directions in a spinning field and are forced to remain inside a hypothetical cylinder. They move and search for other agents to join them by replacing their previous destinations to new targets after finding others.

Positions for making bridges

Understanding Crowds, Grisha Zotov, Thesis Project

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Crowd behaviour and Psychology. The main goal of the project is to develop a design tool which will be able to resolve overcrowded connections in metro stations in different cities

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0. 02 O perations

0.02.3

G e n e tic A l g o r i t h m

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It works on many disciplines such as optimization, machine learning, social systems etc. Recently Genetic algorithms [GA] and neural networks are working togeather toward intelligence, so neural network plays the role of mapping the network from the inputs to the outputs and genetic algorithm with the variations and mutations make the learning process through multiple generations. However GA is a search method used in artificial intelligence and computing, it is used for finding optimized solutions to search problems based on the theory of natural selection and evolutionary biology. Since it has been used in architecture to solve complicated functional problems, it becomes as an advantage for evolutionary computer processes. GA discovered in architectural processes to be one of many approaches for the optimization and evolution, it gives different variations and adaptive solutions and also the evaluating criteria dynamic with the results. The strong inputs, variations, and mutations it receives, the more possibilities and solutions it counts.

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0. 02 O perations Intial population

Ivaliation of fitness

0.02.3 G enet ic A l gor it hm

Selection

Genetic operation

Mutation / Crossover

Evolving Virtual Creatures Karl Sims, 1994

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New population

Criteria is satisfied?

yes

END

no

A research project involving simulated Darwinian evolutions of virtual block creatures, Sims was able to encode a design for these creatures into a virtual DNA. and let them experiment the physical world.

generation of houses Nathaniel Louis Jones

The fitter generation of houses, which are ranked by the lighting, heating and functional criteria. “After a few runs of algorithm, the architect has many fit design options to choose from

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0. 02 O perations

Machine learning [ML] allows computers to find hidden insights without being explicitly programmed where to look, using algorithms that itertively learns from data as inputs. Robotics are the best example of reinforced learning, but its not the intention in this section, neverthless, ML has optimization as a method, it predicts the next option which has not existed before, base on the training.

0.02.4

M a chin e Le a r n i n g

Based on learning method

Based on function

Supervised Learning Classification Regression Unsupervised Learning Clustering Association Semi-Supervised Learning Reinforcement Learning

Regression Algorithms Classification Algorithms Instance-based Algorithms Regularization Algorithms Bayesian Algorithms Clustering Algorithms Artificial Neural Network Algorithms Dimensionality Reduction Algorithms Ensemble Algorithms

Typical Algorithms Linear Regression K-means Clustering K-nearest neighbors Gradient Descent Perceptron Learning Algorithm

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Logistic Regression t-Distributed Stochastic Neighbor Embedding (t-SNE) Markov chain Backpropagation Algorithm

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0. 02 O perations Training Set

0.02.4 Mac hine Lear ning

expected label Machine Learning Algorithm

Feature Vectors

new set

Predictive Model

Expected Label

These experiments follows a supervised learning in which The training data consist of a set of training examples each example is a pair consisting of an input object (vector) and a desired output value (signal).

I.

The learning methods uses a network of boxes with various colors as inputs, and it cuts a range of transformation between the geometries and colors.

II.

Bauhaus theory of 3 colors and 3 primitive shapes

3 Shapes/3 colors

hidden layers neural networks

Network

outputs

inputs

outputs

inputs

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machine learning

Feature Vectors

Label

New Set

training set

Boxes with colors

Computer define new color base on the shape which has the nearly similiar with 3 primitive shapes

hidden layers neural networks

Range of transformation boxes with ranged-colors

Range of transformation boxes with ranged-colors

Network

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0. 02 O perations

0.02.5

D a ta D riv e n F o r m

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The built environment as the societal information process, everything must resonate with everything else, the information which gives the field its charges, these data that responsible for initiate the digital design process, If form follow these data so what it will look like; data is not the perfect in the position that can describe the process, data could be a function too, it could be a way of construction of an idea, and it could be a code which becomes a medium between form and ideas, this medium allows us to define forms via formulas and iterate their topology and geometry, the autonomy of this process allows different types of mutations and variations, which give us a different type of space, a topological space based on bi-continuous deformation and non-linear spatial relationships. Digital computing representation has ignored the ground surface in architecture since XYZ became exchangeable with each other. the now autonomous topological surface assumed a topological space-system, substituting and also ignoring Cartesian reference. This topological space does not serve as a system of measurement and reference as non-Euclidean geometry is contained within a range, constructed, structured, regulated, parameterized and measured against the Cartesian coordinate system.

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0. 02 O perations

0.02.5 d at a Dr iven For m

Euston station massing study, London, 2013 Aedas | R&D Computational Design Research

A form-finding tool that responds to congnitive, experiential site conditions. What the passenger sees approaching or leaving the station affects the massing adjacent to the point.

Eiroa studio based in New York City and Buenos Aires design lead by Pablo Lorenzo-Eiroa

Mรถbius Surface. Klein surface and Boy surface conceptually parameterized against Cartesian coordinate space. identifying the topological displacement that each of these surfaces activate relatively to each Cartesian coordinate space-plane.

grammar of transitions, technical university of munich, 2011-12 Henry Zimmerman, Takahiro ishihara, miguel izaguirre and matthew deutrom

Three types of media informing each other: left: mapping of movement-to-space behaviours; middle: generative computer model developed by students of correlations from mapping; right: scale models exploring spatial properties from generated compositions.

recursive morphologies,2001 Paul coates and Tom Appels

Recursion neatly encapsulates the logic of computation, and Coates's favourite programming language was LISP, and elegant language based on nesting expressions like the recursion itself. He thus developed a series of fractal models like this fractal decomposition with student Tom Appels based on the Belgian theorist Dom Hans Van der Laa's aesthetic catalogue of shapes.

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0.03

0. 01 D es ign p r o p o s al design

proposa l 0 . 0 3 .1

A irports 0 . 0 3 .2 LCA

0. 03 D esign Proposal

0.03.1.1 culture Nodes 0.03.1.2 Masterplan A n a ly s i s 0.03.1.3 case Studies

29

Geometries

A irpo rts

Outputs

Network

0.03.1

Activities

Inputs

30

0.03.1 Air por t s

P r oposition

0. 03 D esign Proposal

Rethinking Airports Heterotopic space was defined by Michel Foucault as “places that are outside places, though they are localizable” He further argues in Different Spaces, that we have moved from localization to extension into our contemporary times’ understanding of place as a case of emplacement. Emplacement differs from earlier understandings of place in being a relational space; “proximity between points or elements” meaning that we need to pay attention to the relations as much as the place itself.

Ursula K. Le Guin's stories in the light of airport spatial understanding, the way the airport terminal is conceived in her collections is an interesting view of cultural space.

0.03.1 .1

C ul tu re N od e s

Justine Lloyd in the Airport Technology he argued that the airport exists as a liminal space, often defined by what it lacks or as nodes along a network. Such a network which, in complexity terms, is not fully distributed as some nodes (airports) are more connected than others.

Airports own multi-functional networks, wide range of services and utilities, a place can be seen as one where transitional spaces create transitional relationships between transitional activities within a network of movements created by multicultural users.

Airports architecture should be out of that context of heterotopic spaces or the functional transitory buildings but should support the sociocultural phenomenon as a very specific public space, it should be rethought with another strategy, it should be close to each city center and not isolated, it should accommodate and communicate in the built environment.

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0. 03 D esign Proposal

0.03.1 Air por t s

London's crazy plan for an elevated airport 1931

Newyork JFK-International Airport 1962

[Architect Eero Saarinen ]

Charles de Gaulle tb1 International airport 1974 33

[Architect Charles Glover ]

[Architect Paul Andreu]

Madrid Barajas tb4 international Airport 2004

[Architect Richard Rogers and Antonio l.]

Singapore Changi tb3 International airport 2008

Beijing tb3 International airport 2008

[Architect SOM ]

[Architect Foster+Partners ]

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0. 03 D esign Proposal

0.03.1 Air por t s

0.03.1 .2

M as terpl an A n a ly s i s

35

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Master Plan Components :

International

Domestic

Regional

Taxiway Runway

AIRSIDE

AIRSIDE Apron

Air Traffic Control Cargo Terminal Building Catering Building Other Buildings

LANDSIDE

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Access Roads and Parking LANDSIDE Terminal Building

Fire Fighting Building Ground Service Equipment Utilities Building

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Master Plan Components :

Operations :

i. Access Interface Departing

Arriving

Runway Taxiway

Parking Circulating

ii. Processing Interface

Apron Ticketing

AIRSIDE

LANDSIDE Ground Service Equipment

Catering Building

ATC Terminal Building

Checking Customs

Check-in

Claiming Baggage

Security Check

Cargo Terminal Building

Utilities Buildings

Fire Fighting Building

iii. Flight Interface Waiting

Un/Loading Baggage

Parking

c Ac

Un/Loading Passenger

ds

oa

sR

es

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Terminal Building Configurations :

AIRSIDE LANDSIDE Arrivals Arrivals

Multi Culture Node

Terminal Building Gates

Gates

Security Check

Security Check

Transit Passengers

departures

Transfer Passengers

Passport and Immigration Control Baggage Claim

Security / offices

Lounges

departure Arrival

iI. Passenger

i. services baggage h. system

41

International

Retails

Customs Control Arrivals Hall

domestic

departure Arrival

Departure Hall

Security Check Passport and Emigration Control Baggage check-in Ticket Counter Departure Entrance departures

42

0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Social& political

Main Design Parameters : i. Aircraft Charachteristics

Airport environmental issues

emission

Capacity

IIi. Level of Services

Noise

los

waste management

Seats

Fuel Capacity Wing Span

d,e,f

comfort operations

Cargo Capacity

Short Distances

iV. Aircraft Traffic and Movement

ii. Annual Passengers peak Hour: Ex.

peak Hour hour by Hour

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Year

no Delays

a,b,c

Length

forecast

Free Flow

1 Flight fo both International: 200p & Domestic: 100p // Departure : 300p Arrival : 300p // P per Hour : 600 P per Year 600*3000 1.800000 m

Take-off landing scheduled non-scheduled

general Aviation military Aircraft

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Runway Configurations :

Runway Shoulder Manoeuvering-Area

Taxiway Movement-Area Holding-Position Stopway 15 km

Wind terrain

i. Singel Runway

aircraft Number

15

[ 1,829m length adequate for aircraft weights 90,718 kg ]

iII. Open V Runway

15°

4

45

aircraft weight

100

6 100 1.5

1000

1:7

150 m flat area

Close less than 700 m between runways.

45

450

Dependent operations away from intersection

Dependent operations toward the intersection

iI. Parallel Runways

iV. Intersecting Runways

.Minimum separation 250 m

crosswind

More that 1310 m, simultaneous take-offs and landings.

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .2 Ma s t e r p la n A n a ly s i s

Apron Configurations : Runway

Taxiway Manoeuvering-Area

Movement-Area Loading Unloading

i. Finger Pier

iII. Linear Concept

60 m di.

Widely adobted ... Low walking distance .... Aircraft constrains .... more parking wih fewer infrastructures

iI. Satellite Concept

singel terminal to process passengers ... high operation cost ... high infrastructure 47

Less infrastructures ... longer walking distance .. low capacity

iV. Transporter Concept

less structure ... Minimizes walking distances ... less taxing time ... high operation cost 48

0. 03 D esign Proposal

0.03.1 Air por t s

0.03.1 .3

C as e S tu d ie s

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50

0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Runways :

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Amsterdam Airport Schiphol

Chicago O’Hare International

Copenhagen Airport

Dallas-Fort Worth International Airport

Denver International Airport

Chicago Midway International

Honolulu International Airport

John F. Kennedy International Airport

Lambert-St. Louis International Airport

LaGuardia Airport

Mitchell International Airport

Narita International Airport

Aéroport Paris-Charles-de-Gaulle

Philadelphia International Airport,

Sydney Airport

Zürich Airport

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Narita International Airport :

DESTINATION (INTERNATIONAL) 36 nations, 2 territories (96 cities) 1725 flights/week

TOTAL AREA 1151 ha

DESTINATION (DOMESTIC) 8 cities 126 flights/week

RUNWAY 1 2500m

TERMINAL 1 440,000 m²² 5 satellites 58 gates

observation deck RESTAURANT

5 FL

DEPARTURE LOBBY check-in counter

4 FL

1

3 FL

RESTAURANT

DEPARTURE LOBBY boarding gate

DEPARTURE LOBBY boarding gate

PASSPORT CONTROL

SHOPPING AREA

SHOPPING AREA

2 FL bus + taxi 1 FL

PASSPORT CONTROL

SHOPPING AREA

ARRIVAL LOBBY

ARRIVAL LOBBY

bus + taxi

TRAIN

BASEMENT

CROSSWIND RUNWAY

DEPARTURE LOBBY check-in counter

lift

stair

lift

stair

lift

3200m

CARGO AREA MAINTENANCE AREA TERMINAL 2 + 3

3 2

Terminal 2: 375,000 m²² 1 satellite 3 domestic gates Terminal 3: 66,000 m²²

RUNWAY 2 4000m

TAXIWAY 53

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport : AIRCRAFT MOVEMENTS 472,950 PASSENGERS 65,933,145

Terminal Building I :

TOTAL AREA 3238ha

The first terminal, designed by Paul Andreu, It consists of a circular terminal building which houses key functions such as check-in counters and baggage claim conveyors. Seven satellites with boarding gates are connected to the central building by underground walkways.

The central building, with a large skylight in its centre, dedicates each floor to a single function. The first floor is reserved for technical operations and not accessible to the public. The second floor contains shops and restaurants, the CDGVAL inter-terminal shuttle train platforms (for Terminal 2 and trains to central Paris) and check-in counters from a recent renovation. The majority of check-in counters, however, are located on the third floor, which also has access to taxi stands, bus stops and special pickup vehicles. Departing passengers with valid boarding passes can reach the fourth floor, which houses duty-free stores and border control posts, for the boarding gates. The fifth floor contains baggage claim conveyors for arriving passengers.

All four upper floors have assigned areas for parking and airline offices.1

TAXIWAY

TERMINAL 1 RUNWAY 2 4215m

55

RUNWAY 1 2700m

HOTEL

TERMINAL 2 RUNWAY 3 2700m

ADMINISTRATOR RUNWAY 4

4200m

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport : Terminal Building I

Paris city center - Airport city

' Image: Google Earth, CDG

' Image: Google Earth, CDG

Terminal Building I [satellite Apron]

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58

0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport : Terminal Building I Functional Plans

59

Level 1 [Sorting baggage]

Level 2 [Services]

Level 3 [Departure]

Level 4 [Transfer]

Level 5 [Arrivals]

Level 6 [Technical]

Level 7,8,9,10 [Parking]

Level 11 [Offices & Visitors] 60

0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport : Terminal Building I Circulation and Movement The proposal to move only the terminal building with its 11th floors from being isolated outside the city to be close to the center of Paris and relocate it in the built environment.

L 3 [Arrivals] L2 [Services&Facilities]

L 2 [Transfer]

City

Airport

satellite Apron

L 1 [Departure]

Level 11 [Offices&Visitors] Level 7,8,9,10 [Parking]

satellite Apron

taxiway Passenger Flow Baggage Flow

61

Level 6 Level 5 Level 4 Level 3 Level 2

[Technical] [Arrivals] [Transfer] [Departure] [Services]

Level 1 [Sorting baggage]

taxiway Passenger Flow

satellite Apron

Baggage Flow

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0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport : Paris downtown Choosing a location in the built environment according to sights, then developing a form according to these viewsheds to create the experience of the passenger's activities within the city Sacre-Coeur de Montmartre

Galeries Lafayette Arc de Triomphe

Musee Grevin Opera Garnier Champs Elysee

Pont Alexandre III Pallais de Chailliot

Ô Chateau Jardin des Tuileries

Bateaux Parisiens Eiffel Tower

Musee du Louvre Musee d'Orsay

Center Pompidou

Pont Neuf Saint Chapelle

Pont Bir-Harkeim

Place des Vosges

Notre-Dame

Jardin du Luxembourg Montparnasse Tower

ATTRACTOR TOURISM POSITIONS - RANKED BY TRIPADVISOR

63

Latin Quarter

Weighted Attractor Dots

64

0. 03 D esign Proposal

0.03.1 Air por t s

0 .0 3 .1 .3 Cas e s t u d i e s

Paris Charles de Gaulle Airport :

VIEW OPTIMIZATION

ViewshedS Analysis

VIEWSHED AT 1600mm

65

VIEWSHED AT 30,000mm

VIEWSHED AT 15,000mm

VIEWSHED AT 40,000mm

66

0. 03 D esign Proposal

0.03.2

L on d o n City A i r p o r t 0.03.2.1 S tu d y o n S i te 0.03.2.2 S tu d y o n S p a ce 0.03.2.3 S tu d y o n F o rm

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68

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0.03.2 LCA

Passenger Activities mapped to departure and arrival

+Travel Preparation +Leaving for the airport

+Travel / home

69

+arrival to the city

+Arrival to the airport

+Leaving the airport

Emigration +Passport/ Security Check

+Check-in

+Say Goodbye

+Baggage check-in

+say welcome

+Baggage reclaim +Customs Hall

+Passport/ Security Check Immigration

+Waiting +Walking to the gate

+Food and Recreational activities

+Food and Recreational activities

+Bank/info

+Boarding

+Walking to the services +Gates

+Departure

+arrival

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0. 03 D esign Proposal

0.03.2 LCA

London City Airport

LCA Expansion Plan (2006-2021)

It has been selected because it owns the qualities that can investigate the idea behind culture nodes, it's surrounded by views and marks and it's not isolated from the urban fabric.

2011: 70.000 flight movements & 3 milion passengers.

Goals: 120.000 flight movements & 4,7 milion passengers.

20 minute check in time from door to gate and 10 minute arrival time from tarmac to train.

E X I ST P R OPOSAL Reconfigured Airport Entrance/Forecourt

A s part of the extension to the terminal building , the entrance for ecourt area is being configured to bette r use the space available . Taxi parking , vehicle drop-off/pick-up and bus ser vices will be moved to the east of their current location , whe r e City A viation House is currently.

Reconfigured Airport Entrance/Forecourt

C ity A viation House, our current office building would be demolished and office facilities moved into new offices beside the existing terminal.

Hotel

P lans for a new hotel beside the remodelled ai rport entrance for ecourt are being drawn up. This would meet a strong demand for new hotel space in the local area.

New Offices

C ity A viation House, our current office building would be demolished and office facilities moved into new offices beside the existing terminal.

Reconfigure Passenger and Staff Parking

I n o r der to bette r use the space at the front of the airpo rt, sho r t and long term passenge r parking , as well as staff pa r king , would be moved further east .

Other

a new fi re station , emergency access point and baggage p r ocessing area.

Terminal building

' Image: Google Earth, lCA

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0 .0 3 .2 .1 s tud y o n s i t e

Train station Hotel plot

Terminal building runway Parking area

Plot for parking

Strategy

Plot for the extesion

73

I Operation: Optimization III Operation: Behavioral Condition: Views Condition: Crowds / Movement Function: Using three-dimensional Function: Creating a parametric model for finding a generic form targeted agents simulation to predict movements in different based on basic site analysis, scenarios, developed to get the the proposed program, and targeted viewsheds. fewer intersections with fast and directed paths.

II Operation: Genetic Algorithm IV Operation: AI Condition: Activities pattern Condition: Form selection Function: Selection process, Function: Dividing the complex network in between different crossing over and mutation; Viewsheds quality, preliminary functions to small simple tasks as a way for developing an AI area plots, and the shortest distance between connections, aided design movement system between these activities and as a fitness values for the evaluation. functions.

Terminal building Plot for Hotel

' Image: Google Earth, LCA

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0 .0 3 .2 .1 s tud y o n s i t e

Connectivity and Visibility Analysis to the site

Using depthmapx to check the access and visibility points to the plot

75

Visibility analysis, the blue more closed areas and less accessible

Isovest, the red axes are more longer directed to the views

Connectivity analysis, the red axes are more connected and reached

Viewsheds, the red are more open to the views 76

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0.03.2 LCA

0 .0 3 .2 .1 s tud y o n s i t e

SIte | infrastructure model Define the site plot

Operation: Crowds | access points | buildings

This process will be discussed clearly in the next chapter.

The network between Terminal building and train station, parking, and hotel, created by a agent based simulation for the crowds in a choosed peak hour of 600 passengers, the access points to the site placed as sources and the buildings as targets in different levels.

Generated Netwrok

AIR SIDE LAND SIDE

Hotel

Train Station

ARRIVAL EXIT LEVEL

DEPARTURE ENTRY LEVEL

Parking

AIR SIDE LAND SIDE

77

Terminal

network it01 targets and sources [rules discussed in the next chapter] 78

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0 .0 3 .2 .1 s tud y o n s i t e

ATTRACTOR TOURISM POSITIONS - RANKED BY TRIPADVISOR Viewsheds

m

3000

m

2000

DOCKLAND EQUESTRIAN CENTER

NEW BECKTON PARK

m

1000

NEWHAM CITY FARM

ROYAL VICTORIA GARDEN

500m

GREENWICH HERITAGE CENTER

JURASSIC KINGDOM

WOOLWICK FERRY MUSEUM OF CULINARY HISTORY

EXCEL LONDON BOWCHEEK ECOLOGY PARK THE THAMES BARRIER

TRINITY BUOY WHARF

LONDON O2

MARYON WILSON ANIMAL PARK GREENWICH PENINSULA ECOLOGY PARK

' Image: Google Earth,LCA

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80

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0 .0 3 .2 .1 s tud y o n s i t e

Aa1

Parametric Model

Ba2

Ba1 A a2

Define the site plot

Viewshed

a1

Aa1 Proposal:

Generic modelto the views

Train station a1 Vertical Connection a2 Departure entrance & information Restaurant & b1 shopping b2 Transfer c1

Restaurant & shopping

c2 Arrival H

a2

Ca1

TRAIN STATION

TERMINAL

Da2

H

Ca2

LEVEL 1 Ab2

Site with existing terminal

Ab1 Db1

b1

Bb1

H

Db2 Cb2

LEVEL 2

Ac1

Hotel

Bc1

c1 c2

Cc1

LEVEL 3

Bc2

Ac2

Dc1 H

Bb2

b2

Cb1

Green space

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Optimization

Dc2

Cc2 Random footprints according to old terminal analysis 82

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0 .0 3 .2 .1 s tud y o n s i t e

Optimization

Optimization

Viewshed

Viewshed

Evolutionary algorithm

According to the criteria of choosing views, the generic model initialized and evaluated to these different directional views in the built environment. fitness value:

View quality.

Area plots.

Shortest distance of connections.

Iniitialization Mutation Selection Cross-over

blocked builidngs target views

Termination

minimal distance

vertical connection open to tager views

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84

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0.03.2 LCA

0 .0 3 .2 .1 s tud y o n s i t e

Environmental Analysis Sun rays & Wind direction Model Selection Spaces Orientation

Sunpath- Drybulb Temperature Diagram

85

Sunpath Diagram

Windrose

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0.03.2 LCA

0 .0 3 .2 .1 s tud y o n s i t e

Environmental Analysis Sun rays & Wind direction Model Selection Spaces Orientation

Temperature By Hour

Relative Humidity

Direct Normal Radiation 87

Generated Models

88

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0 .0 3 .2 .1 s tud y o n s i t e

generic Model evaluation

Blocked Viewrays | Area Plot | Distance

0.03.2 LCA

BLOCKED VIEWRAYS

AREA PLOT (m2)

DISTANCE (m)

529323.90 321107.21 206409.11 188112.58 313081.68 21410.92 390310.62

2250.09 2181.69 4076.32 1190.01 4915.03 870.60 3908.13

5.1 to 9.2

495143,11 305054,39 184571,21 206335,15 335141,38 93761,05 457282,02

2213.16 2343.40 4097.08 1151.71 4842.31 839.30 3901.93

7.7 to 10.6

456060,59 350635,21 172125,52 199696,57 221956,59 295931,87

2229.47 2266.34 4137.23 1229.85 4926 917.47 3899.07

6.6 to 11.7

431277.36 419991.36 212138.46 1947437.57 283898.89 92139.21 481461.54

2227.77 2263.10 4055.12 1263.31 4951.10 1013.84 3803.46

6.5 to 9.7

232482.47 174649.42 128525.33 103034.68 78754.85 115267.47

2254.21 2255.32 4202.41 1249.31 4918.07 3804.75

6.7 to 8.4

493336.22 431548.48 239365.67 162144.88 256433.85 88648.76 504061.65

2218.39 2435.67 4104.56 1218.45 4944.12 975.48 2836.46

8.1 to 12.6

431330.24 337910.97 179938.13 165045.11 359487.36 45718.45 535269.28

2204.95 2010.09 4183.06 1211.68 4884.76 1038.56 3817.37

7.9 to 12.1

Model Selection

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90

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Terminal building

Study on Program and Circulation Model Selection development

Terminal Boundaries Evolution 0.04 Grid / Solid Addition

Second Floor Boundry Viewshed Axes Terminal Boundaries Evolution 0.03 2 separate circulations

Floors Projections

Selected Model Departure Pier

Arrival Pier

Operation: Connectivity

Adding the existing departures and arrivals piers from the apron side plots to the model boundries, then making the generic connectivity axes between the hotel, the train station, and the parking, to the terminal building.

First Floor Boundry Viewshed Axes

Terminal Boundaries Evolution 0.02 subtraction / Adding site axes

Site constrains

Departure Pier Arrival Pier

Connectivity Axes Train Station

Terminal

Parking

Hotel Operation: Continuity

Terminal Boundaries Evolution starts from the generic model boundaries, making continues looping in circulations, and divide them according to the crowds circulation into two separate flows between arrivals and departures then adding the site constraints and grid then solid mass of the generic model.

91

Ground Floor Boundry Viewshed Axes

Terminal Boundaries Evolution 0.01 Continuous axes / Adding piers 92

0. 03 D esign Proposal

0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e AIR SIDE LAND SIDE

Terminal building

Study on Program and Circulation Hotel

Model Selection development

Train Station

Operation: Views & Circulation

According to the operation of subtractions and addition of the axes and solid mass from the generic model, the viewsheds and circulation operation added to create the relation between spatial experience in the terminal and the built environment.

[Air side / land side]

LAND SIDE SERVICES level LEVEL Services

Hotel

LOGISTICS LEVEL Logistics Level

AIR SIDE

LAND SIDE

Schematic Longitudinal Section Circulation

Train Station

ARRIVAL LEVEL Arrival level

Parking

DEPARTURE ENTRY LEVEL

Apron

AIR SIDE LAND SIDE

Schematic Cross-Section Network / Solid Mass

DEPARTURE LEVEL Departure level

AIR SIDE

Parking

ARRIVAL EXIT LEVEL

DEPARTURE HALL LEVEL

ARRIVAL ENTRY LEVEL

Apron

[Air side / land side]

[ARRIVAL CIRCULATION] [Air side] [land side]

[DEPARTURE CIRCULATION]

Schematic Cross-Section Network / Flow [Air side / land side]

Arrival Exit level

Departure Exit level

Schematic Plan Circulation

[Air side / land side]

[land side]

[Air side]

Departure level Arrival level Departure level 93

Arrival level

Departure Entry level

Arrival Entry level 94

0. 03 D esign Proposal

0.03.2 LCA

Terminal building

GATES [FROM UPPER LEVEL]

Study on Program and Circulation

PRE DEPARTURE LOUNGE

GATES

0 .0 3 .2 .2 s tud y o n s p a c e

CENTRALIZED SECURITY CHECK POINT

Circulation development SERVICES

SECURITY CHECK ENTRANCE [FROM LOWER LEVEL]

ENTRANCE [DEPARTURE]

Linear circulation, entrance level in lower level from the eastern airside gates, crossing all check-points activities for the passenger to reach the land-side in a higher level, except the transfers direct access to the airside.

The typology of the circulation for the arrivals intended to create a spatial experince between inside and outside without making a conflict toward the departures circulation.

SERVICES

GATES [FROM LOWER LEVEL]

ARRIVAL LOBBY

PASSPORT CONTROL [IMMIGRATION]

INFO. [ARRIVALS] BAGGAGE RECLAIM HALL

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CHECK-IN HALL

GATES

Arrivals Circulation

PASSPORT CONTROL [EMIGRATION]

INFO. [DEPARTURE]

Transfer

Linear circulation, the transition between entrance level in lower level from the western land-side entrances, crossing all check-points activities for the passenger to reach the air-side in a higher level, except the domestic direct access from the check-in halls to the airside.

The typology of the circulation for the departures intended to create a spatial experince between inside and outside without making a conflict toward the arrivals circulation.

DOMESTIC

Departures Circulation

EXIT [ARRIVALS HALL]

EXIT [FROM UPPER LEVEL]

CUSTOMS HALL

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0. 03 D esign Proposal

0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e

Terminal building Study on Circulation

Infrastructure:

Operations:

i. Buildings Network [Site]

Crowd Behaviour [ Movement]

AI [ spatial experience]

i. Access Interface

Agents based system

Attraction to Pheromones

Vision

Predefined: Access Nodes Obstacles Targets

Parking Circulating

Turning Speed ii. Passengers Network [Departure]

circulation

Vision Attraction to boundry

Avoiding Obstacles

Pheromones represents agent trails

Network Structure

DEPARTURE ENTRY LEVEL

Ticketing Checking Customs

Check-in

DEPARTURE HALL LEVEL

iii. Passengers Network [Arrivals]

View sheds

iii. Flight Interface

ARRIVAL ENTRY LEVEL

Avoiding Obstacles

Waiting

Un/Loading Baggage

Attraction to boundry Cohesion [Keeps agents togeather]

Separation [Maintains crowd]

Alignment [Maintains movement]

Avoidance [Prevent collisions]

Seeking [Towards a target]

Claiming Baggage

Security Check

Attraction to Pheromones

Vision

ARRIVAL EXIT LEVEL

AIR SIDE LAND SIDE

Turning Speed

ii. Processing Interface

Terminal Building AIR SIDE LAND SIDE

Boids Pheromones

Avoiding Obstacles

Departing

Arriving

Clearence [maximize field of view]

Un/Loading Passenger Linear circulation [Keeps movement organized]

Hierarchy [Keeps priorities in sequence] 1

Steering [maintain orientation to views]

Variation [functional colors and shapes] [Avoiding queuing in checkpoints]

3 2

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Terminal building Study on Circulation

REMAPPING ACTIVITIES INTO SMALL SUBDIVISIONS

AI [ spatial experience]

SELF SERVICE CHECKPOINTS

[ AI Tasks ]

Operation: Processing interface | shortest walk, flexibility Partial plan genesis INFORMATION POINT

The idea to divide a main big operation -for reference- in the departure circulation to a small self services points and rearrange them in the floor plan, mapping the activities that happen once entering the terminal from the entrance's information desk, security check, ticketing, check-in, boarding pass, and baggage drop to be fragmented in the whole space and oriented to reduce the the flow velocity. Using AI system and biometric technologies enhance the idea tward automation, to map the flow and orient passengers directly and in sequence to the check-in points that calculated and repeated in the whole floor planning, which will mantain the shortest walk to the passenger and the flexibility of the circulation flow and apart from the methodology of counters row and passengers' queuing.CENTRALIZED SECURITY GATES

PRE DEPARTURE LOUNGE

GATES [FROM UPPER LEVEL]

TICKETING

SECURITY CHECKPOINT (Body Scanning)

PASS (Biom

CHIECK-IN POINT (Biometrics Technology)

SECU (Bod

CHECK POINT

REMAPPING ACTIVITIES INTO SMALL SUBDIVISIONS

SELF SERVICE CHECKPOINTS

[ AI Tasks ]

SERVICES

BAGGAGE DROP (Biometrics Technology)

DOMESTIC

INFORMATION POINT

SECURITY CHECK ENTRANCE [DEPARTURE]

CE LOWER

PASSPORT CONTROL [EMIGRATION]

TICKETING

SHORTEST WALK | LONGEST WALK CHECK POINTS & BOUNDARIES

CHECK-IN HALL

INFO. [DEPARTURE]

SECURITY CHECKPOINT (Body Scanning)

PASSPORT CONTROL (Biometrics Technology)

CHIECK-IN POINT (Biometrics Technology)

SECURITY CHECKPOINT (Body Scanning)

SHOPS & LOUNGES

BAGGAGE DROP (Biometrics Technology)

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100 SHORTEST WALK | LONGEST WALK CHECK POINTS & BOUNDARIES

0. 03 D esign Proposal

0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e

Terminal building Study on Circulation

AI [ spatial experience]

Operation: Processing interface Partial plan genesis

This diagram shows a random placement for the self service checkpoints into the partial plan, the dynamic field represents an un-real flow of the passengers but it maintain all the possibilities of the directional flow to keep the circulation very directed and dynamic toward all service points, between linear and non-linear different processes zones.

Security Check

Lounge Lounge

Check-in Information

Ticketing Security Check

Baggage Drop

Baggage Drop Security Check Security Check

101

Security Check

Check-in

Lounge

Check-in

Lounge

Directed Dynamic Movement Services in a non-linear zones Less intersections happening once in the netrance area

Information

Ticketing Lounge

Directed Dynamic Movement Services in a Linear zone

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0. 03 D esign Proposal

0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e

Terminal building Study on Circulation

Crowd behaviour [ movement ]

Departures: netwrok Partial plan genesis

Following the pre-definied rules of a crowd circulation for the departures by defining all access nodes for the passengers and the checkpoints as a targets.

The initial grid created from the partial plan to place the Source nodes, Targets and Obstacles.

Generic Grid

Obstacles

obstacles nodes network

Predefined:

Open spaces / Circulation

103

Solid and Void

Boids Pheromones

Access Nodes Obstacles Targets

Vision Attraction to boundry

Avoiding Obstacles Turning Speed Cohesion [Keeps agents togeather]

Separation [Maintains crowd]

Alignment [Maintains movement]

Attraction to Pheromones Avoidance [Prevent collisions]

Crowd Analysis Partial plan zone, iteration 0.1

Seeking [Towards a target]

Clearence [maximize field of view]

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0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e

Terminal building Variables

Study on Circulation

Crowd behaviour [ movement ]

Avoiding Obstacles

Vision

Attraction to

Attraction to Views Attraction to

targets Departures: netwrok Pheromones Partial plan genesis According to the directed dynamic movement diagram, sources as access nodes, obstacles to direct the agents to the views, and targets for the services nodes, all placed in the real partial plan, and the simulation run for 22 iterations to track 300 passenger movement.

105

Crowd Analysis Partial plan zone, iterations

Crowd Analysis Partial plan zone, iterations

106

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0.03.2 LCA

0 .0 3 .2 .2 s tud y o n s p a c e

Terminal building Study on Circulation

Crowd behaviour [ movement ]

Departures: netwrok Partial plan genesis The selection for the network according to the maximum intersection nodes and maximum attraction to the views with the lowest attraction to the pheromones to be developed after according to the generic netwrok analysis that descriped previously.

Sources as access nodes.

Obstacles direct the agents to the views.

Targets for the services nodes.

Intersections translated to activities Obstacles translated into floor opening

107

User Pattern

Obstacles

+Shapes & Colors: The system will learn different pattern from the passnger flow movement, and giving them different colors to evaluate and decide which is the best flow that move between the maxiimum targets to the views and pass to all check points at the same time, then the system can inform after to the best arrangement for the check points to the viewsheds

Crowd Analysis Passenger flow patterns variations

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Passenger pattern

Terminal building Study on Circulation

+Velocity: High movement speed locations translated into slopes in plan which all located around obstacles (artiums).

Low movement speed locations translated into slopes directed to the views.

Crowd behaviour [ movement ]

Departures: netwrok Partial plan genesis

Atriums

Selected User Pattern, obstacles, and intersections. All translated into the partial plan model circulation.

+Density: High density areas around checkpoints destructed by placing more attractors to the view in the boundries.

Access Nodes Entrances & Access to lounges

Floor Opening Floor Opening

Floor Opening Floor Opening

Floor Opening

Checkpoints Baggage Drop

Baggage Drop

Ticketing

Check-in Check-Point

Baggage Drop

Check-Point

Check-in

Check-Point

Lounges

Check-Point Check-Point

109

Information

110

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0 .0 3 .2 .3 s tu d y o n F o r m

Terminal building

Study on Form structure Ramps and Views

Operation: extrusion

Heights boundaries are limited from the airside to the landside regarding the apron and runway design rules.

Extrusions as the simplest operation to have the flixability to not break the heights and to acheive the funcutional diagram to a multible level toward circulation and views analysis.

Arrival Exit level

[DEPARTURE CIRCULATION] [land side]

Departure Entry level

[ARRIVAL CIRCULATION] [Air side] [land side]

[DEPARTURE CIRCULATION]

[ARRIVAL CIRCULATION]

Departure Exit level

Preliminary Form Structure Circulation | Function [Air side / land side]

[Air side]

Arrival Entry level

Departure level Arrival level Services level

Services level

Departure level

Arrival level

Arrivals

Departure

Schematic Section Circulation | Function [Air side / land side]

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112

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Terminal building

Study on Form structure Ramps and Views

Train Station Ů? Services Level

Hotel Terminal Building

Parking Area

Recreational Level

Ů? Services Level (BHS)

Facilities Level

Apron Side

Preliminary Form Structure Circulation | Function [Air side / land side]

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114

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0 .0 3 .2 .3 s tu d y o n F o r m

Terminal building

Study on Form structure

Preliminary Form Structure Circulation | Functional Section

Preliminary Form Structure Circulation | Functional Interior 115

Preliminary Form Structure Circulation | Departure pier Structure allows visibility without access 116

0. 03 D esign Proposal

0.03.2 LCA

0 .0 3 .2 .3 s tu d y o n F o r m

Terminal building

Recreational Level

Study on Form structure

Departure Level

Shops

Shops

Restaurants

Lounges Lounges

Lounges

s

e

vic

Ser

es acc

Multi-Activities

BHS

Baggage Handling System Level

Preliminary Form Structure Priliminary Elevations Study | Structure | Departure pier 117

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0 .0 3 .2 .3 s tu d y o n F o r m

The previous form finding studies to generate a conceptual mass that represents functions and circulation in primitive geometries, but the result of the form morphology is not processed yet.

119

120

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0 .0 3 .2 .3 s tu d y o n F o r m

Terminal building

Study on Form structure To be continued toward a complex mutation across the structure and apart from the circulation.

Form Structure Study in the initial Generic Model [pixelated columns | treads structure mutation] 121

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0.04

c o n cl u s io n 0.04.1 Vi s i o n 0.04.2 Imp l e ment at i ons

0. 04 c onclusion 0.04.1

Visio n In these sequence of experiments, a pre-defined input inherits buildings, it is designed according to several aspects such as human behavior in motion, flexible circulation and the spatial experiment in the built environment, following certain rules is the behavioural norm, finding the form genetically approached parallel to the experiments, an exist program implemented to enhance the design process, and AI system controls the proposed networks. In general, the autonomy of this processes with the vestigial evolution allows different types of mutations and variations, but considering the research being based on prediction in simulations as an approach for developing the proposed idea in airports terminals buildings, is not quite clear to get a functional network, because in theory you don't need to watch the simulation in order to find out the result, however simulations could be able to encode a design process into a non-predicted approach, they are unexpected and self-organized by the proposed rules, but the difficulties appear when it comes to letting them experiment the physical world, but the criterion in these process is the efficient rules that need the human's brain to evaluate, nevertheless the necessity of the incorporation of both the top-down, and bottom-up method in design becomes very efficient if it is following an informative structure for the evaluation strategy.

In particular, the use of evolutionary processes in design, AI, Genetic algorithms, behavioral and self-organizing systems, are facing a real challenge for the development of form-function model, the representation of these two information types is difficult for a digitalized process. However, it could aid in the design strategies and prepare for a more structured design processes, but not to get the result of an architectural combined model. A distributed approach, based on a network of intelligent objects, may be more suitable, or the data that is used to represent function and use, can be relocated within different types of constructs while linked to-

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each other, such as the way an object can find its way of seeing by defining an attribute that gives him all ideas about the environment and a local responses to response for it as the desired action; But still they are the desired actions that predefined and re-evaluated from a past experiences.

Add on top of these processes, the typology of the spaces I chose such as airports terminal buildings, proposing it as a multi-cultural node, could be evaluated by these strategies and can be controlled by an intelligent system, but it can't be designed based on accuracy of predictions, it can re-evaluated by these outputs for a several futuristic iterations in the design as a preliminary approach through a computer code that can grow, evolve and mutate architectural matter without the burden of preconceived ideas of top-down process.

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Im pl em ent at ions Architecture is neither a collection of things or a set of rules and codes16, AI, ML, GA, ... all these definitions could be applied in a visionary processes, 2d representations, stages in design analysis, or in a virtual reality world, but in a 3d architectural space they fail, they are not limited and not predicted, they can't delegate thinking between human behaviour and reactions, it's possible to give a response but not in a real dimensional space.

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0. 05 c redits 0.05.1

B ib l iog rap h y Books: 1. Russell, J. Stuart and Norvig, Peter. Artificial Intelligence: A Modern Approach. 3rd ed. New Jersey: Prentice Hall, 1995.

2. Negroponte, N. From Soft Architecture Machines. Cambridge: MIT Press, 1975.

3. Spiller, Neil. Visionary Architecture: Blueprints of the Modern Imagination. New York: Thames & Hudson, 2007.

4. Sims, Karl. Evolving Virtual Creatures. Thinking Machines Corporation. Cambridge, 1994.

5. Williams, James. Deleuze’s Ontology and Creativity: Becoming in Architecture. 2000.

6. Eiroa, Pablo Lorenzo and Sprecher, Aaron. Architecture In Formation. Routledge, 2013.

7. Spyropoulos, Theodore and Frazer, John and Schumacher, Patrik.

Adaptive Ecologies: Correlated systems of Living. London: AA, 2013.

8. De Landa, Manuel. Deleuze and the Use of the Genetic Algorithm.

MIT Press, 2004.

9. Frazer, John. An evolutionary architecture. London: AA, 1995.

10. Reiser, Jesse. Atlas of Novel Tectonics. Princeton Architectural press, NY, 2006.

11. Shiffman, D. The Nature of Code. Self published, 2012.

12. De Landa, Manuel. A Thousand Years of Nonlinear History. MIT press, 1997.

13. Green, E. Keith. Architectural Robotics: Ecosystems of Bits, Bytes, and Biology. MIT press, 2016.

14. Johnson, Steven. Emergence: The connected lives of ants, brains and software. Penguin press, 2001.

15. Koolhass. R. S,M,L,XL. NY, Monacelli Press, 1995.

16. Picon, Antoine. Architecture and the Sciences: Exchanging Metaphors. Princeton Architectural Press, 2003.

Papers & Lectures: 1. The Y Factory. “Transforming the city.” Barba project, Life in the Fully Adaptable Environment, 2015.

2. Francois, Roche and Camille, Lacadee. “Machines for Rent: Experiments by New-Territories.” AD, 2014.

3. Seleem. Mostafa. “Embedded Pattern.” MA, DIA, 2016.

4. Pietzner, Djamila. “Techniques of automated design in architecture.”

MA, DIA, 2016.

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5. Mckenzie, Henry. "The Internet of Architecture Things." MA, DIA, 2016. http://designdose.com/ 6. R4D4. “Wired.” AADRL, V04, 2015.

7. Research clusters 2-6. “March GAD.” UCL, 2014.

8. Gramazio, F. and Kohler, M. “Robotic fabrication of acoustic brick walls.” ETH Zurich, 2014.

9. Tachi, Tomohiro. “Generalization of Rigid Foldable Quadrilateral Mesh Origami.” University of Tokyo,2009.

10. Frazer, John. "Genetic Algorithms and Evolutionary Computing."

Lecture 2014.

11. Lynn, Greg. "Conversation about Digital Archaeology." 2013.

12. William, j. Mitchell. “Techniques of automated design in architecture.” University of California, 2016.

Links: 1. en.wikipedia.org 2. Lomas, Andy. “Cellular Form Generation test.” https://www. youtube.com/watch?v=kcEBEIAhsuk 3. Lomas, Andy. “Growth by Aggregation.” https://vimeo. com/83297099 4. Lomas, Andy. “Cellular forms.” https://vimeo.com/83294152 / https://vimeo.com/82989945 5. Hansmeyer, M. “Platonic Solids.” http://www.michael-hansmeyer. com/projects/platonic_solids.html?screenSize=1&color=0#3 6. Jing S. “Architectural evolutionary system based on Genetic Algorithms.” http://www.interactivearchitecture.org/architecturalevolutionary-system-based-on-genetic-algorithms.html 7. “Artificial Intelligence.” http://archinect.com/news/tag/566665/ artificial-intelligence 8. Deskriptiv Gbr. “A unified Approach to grown structures.” https://www.youtube.com/watch?v=9HI8FerKr6Q&index=34&lis t=LLMccHc2tq6Bif1CVw8_4jTg&t=171s 9. Google. “Google Autonomous car.” https://waymo.com/tech/ 10. Ebyass. “Kinetic Paper Wall.” https://www.youtube.com/wat ch?v=Ua47OHFLJV4&list=LLMccHc2tq6Bif1CVw8_4jTg&ind ex=111 11. Sci-Arc. “Advances in Architectural Geometry.” https://www. youtube.com/watch?v=iKFZO75BmLA&t=325s

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